SOTAVerified

Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

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Showing 901–950 of 1718 papers

TitleStatusHype
SocialLight: Distributed Cooperation Learning towards Network-Wide Traffic Signal Control—0
Mastering Asymmetrical Multiplayer Game with Multi-Agent Asymmetric-Evolution Reinforcement Learning—0
Interpretability for Conditional Coordinated Behavior in Multi-Agent Reinforcement Learning—0
Graph Exploration for Effective Multi-agent Q-Learning—0
Cooperative Multi-Agent Reinforcement Learning for Inventory Management—0
Sustainable AIGC Workload Scheduling of Geo-Distributed Data Centers: A Multi-Agent Reinforcement Learning Approach—0
Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learning—0
Learning to Communicate and Collaborate in a Competitive Multi-Agent Setup to Clean the Ocean from Macroplastics—0
Multi-agent Policy Reciprocity with Theoretical Guarantee—0
MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning—0
Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning—0
Risk-Aware Distributed Multi-Agent Reinforcement Learning—0
Regularization of the policy updates for stabilizing Mean Field Games—0
MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering—0
Selective Reincarnation: Offline-to-Online Multi-Agent Reinforcement Learning—0
DeepHive: A multi-agent reinforcement learning approach for automated discovery of swarm-based optimization policies—0
Multi-Agent Reinforcement Learning with Action Masking for UAV-enabled Mobile CommunicationsCode0
The challenge of redundancy on multi-agent value factorisation—0
Embedding Contextual Information through Reward Shaping in Multi-Agent Learning: A Case Study from Google Football—0
Learning Reward Machines in Cooperative Multi-Agent Tasks—0
Causality Detection for Efficient Multi-Agent Reinforcement Learning—0
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games—0
Large-Scale Traffic Signal Control Using Constrained Network Partition and Adaptive Deep Reinforcement Learning—0
Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning—0
Major-Minor Mean Field Multi-Agent Reinforcement Learning—0
Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning—0
A New Policy Iteration Algorithm For Reinforcement Learning in Zero-Sum Markov Games—0
SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning—0
Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic GamesCode0
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement LearningCode0
MAHTM: A Multi-Agent Framework for Hierarchical Transactive MicrogridsCode0
Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning—0
Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networks—0
Solving routing problems for multiple cooperative Unmanned Aerial Vehicles using Transformer networks, vol. 122, pp. 106085, 2023Code0
Evolutionary Reinforcement Learning: A Survey—0
Proactive Multi-Camera Collaboration For 3D Human Pose Estimation—0
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning—0
PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities—0
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning—0
Approximating Energy Market Clearing and Bidding With Model-Based Reinforcement Learning—0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning—0
GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement LearningCode0
Distributed Learning Meets 6G: A Communication and Computing Perspective—0
Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning—0
A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning—0
Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation—0
On the Role of Emergent Communication for Social Learning in Multi-Agent Reinforcement Learning—0
IQ-Flow: Mechanism Design for Inducing Cooperative Behavior to Self-Interested Agents in Sequential Social DilemmasCode0
Multi-Agent Reinforcement Learning for Pragmatic Communication and Control—0
Combating Uncertainties in Wind and Distributed PV Energy Sources Using Integrated Reinforcement Learning and Time-Series Forecasting—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified